
pip install pandas

Requirement already satisfied: pandas in d:\anaconda\lib\site-packages (2.0.3)
Requirement already satisfied: python-dateutil>=2.8.2 in d:\anaconda\lib\site-packages (from pandas) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in d:\anaconda\lib\site-packages (from pandas) (2023.3.post1)
Requirement already satisfied: tzdata>=2022.1 in d:\anaconda\lib\site-packages (from pandas) (2023.3)
Requirement already satisfied: numpy>=1.21.0 in d:\anaconda\lib\site-packages (from pandas) (1.24.3)
Requirement already satisfied: six>=1.5 in d:\anaconda\lib\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)
Note: you may need to restart the kernel to use updated packages.


pip install numpy

Requirement already satisfied: numpy in d:\anaconda\lib\site-packages (1.24.3)
Note: you may need to restart the kernel to use updated packages.


pip install plotly

Requirement already satisfied: plotly in d:\anaconda\lib\site-packages (5.9.0)
Requirement already satisfied: tenacity>=6.2.0 in d:\anaconda\lib\site-packages (from plotly) (8.2.2)
Note: you may need to restart the kernel to use updated packages.


pip install sklearn

Collecting sklearnNote: you may need to restart the kernel to use updated packages.

  error: subprocess-exited-with-error
  
  python setup.py egg_info did not run successfully.
  exit code: 1
  
  [15 lines of output]
  The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
  rather than 'sklearn' for pip commands.
  
  Here is how to fix this error in the main use cases:
  - use 'pip install scikit-learn' rather than 'pip install sklearn'
  - replace 'sklearn' by 'scikit-learn' in your pip requirements files
    (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
  - if the 'sklearn' package is used by one of your dependencies,
    it would be great if you take some time to track which package uses
    'sklearn' instead of 'scikit-learn' and report it to their issue tracker
  - as a last resort, set the environment variable
    SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True to avoid this error
  
  More information is available at
  https://github.com/scikit-learn/sklearn-pypi-package
  [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

Encountered error while generating package metadata.

See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

  Downloading sklearn-0.0.post12.tar.gz (2.6 kB)
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'error'


import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC


df = pd.read_csv('D:/MY DESTINATION/PROJECTS/Projects/Cancer_breast_survive_prediction/BRCA.csv')


df.head()


df.isnull().sum()

Patient_ID             7
Age                    7
Gender                 7
Protein1               7
Protein2               7
Protein3               7
Protein4               7
Tumour_Stage           7
Histology              7
ER status              7
PR status              7
HER2 status            7
Surgery_type           7
Date_of_Surgery        7
Date_of_Last_Visit    24
Patient_Status        20
dtype: int64


df.shape

(341, 16)


df= df.dropna()


df.isnull().sum()

Patient_ID            0
Age                   0
Gender                0
Protein1              0
Protein2              0
Protein3              0
Protein4              0
Tumour_Stage          0
Histology             0
ER status             0
PR status             0
HER2 status           0
Surgery_type          0
Date_of_Surgery       0
Date_of_Last_Visit    0
Patient_Status        0
dtype: int64


df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 317 entries, 0 to 333
Data columns (total 16 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Patient_ID          317 non-null    object 
 1   Age                 317 non-null    float64
 2   Gender              317 non-null    object 
 3   Protein1            317 non-null    float64
 4   Protein2            317 non-null    float64
 5   Protein3            317 non-null    float64
 6   Protein4            317 non-null    float64
 7   Tumour_Stage        317 non-null    object 
 8   Histology           317 non-null    object 
 9   ER status           317 non-null    object 
 10  PR status           317 non-null    object 
 11  HER2 status         317 non-null    object 
 12  Surgery_type        317 non-null    object 
 13  Date_of_Surgery     317 non-null    object 
 14  Date_of_Last_Visit  317 non-null    object 
 15  Patient_Status      317 non-null    object 
dtypes: float64(5), object(11)
memory usage: 42.1+ KB


#Gender column to see how many females and males are there

print(df.Gender.value_counts())

Gender
FEMALE    313
MALE        4
Name: count, dtype: int64


# stage of tumour of the patients
stage  =  df["Tumour_Stage"].value_counts()
print(stage)

Tumour_Stage
II     180
III     77
I       60
Name: count, dtype: int64


stage  =  df["Tumour_Stage"].value_counts()
transactions = stage.index
quantity = stage.values
figure = px.pie(df, values=quantity, names= transactions, hole=0.5, title="Tumour Stages of Patients" )
figure.show()


# Histology
histology = df["Histology"].value_counts()
transactions = histology.index
quantity = histology.values
figure = px.pie(df, 
             values=quantity, 
             names=transactions,hole = 0.5, 
             title="Histology of Patients")
figure.show()


#ER status, PR status, and HER2 status of the patients
# ER status
print(df["ER status"].value_counts())
# PR status
print(df["PR status"].value_counts())
# HER2 status
print(df["HER2 status"].value_counts())

ER status
Positive    317
Name: count, dtype: int64
PR status
Positive    317
Name: count, dtype: int64
HER2 status
Negative    288
Positive     29
Name: count, dtype: int64


#surgery_type

# Surgery_type
surgery = df["Surgery_type"].value_counts()
transactions = surgery.index
quantity = surgery.values
figure = px.pie(df, 
             values=quantity, 
             names=transactions,hole = 0.5, 
             title="Type of Surgery of Patients")
figure.show()


df.head()


df["Tumour_Stage"] = df["Tumour_Stage"].map({"I": 1, "II": 2, "III": 3})


df.head()


df["Histology"] = df["Histology"].map({"Infiltrating Ductal Carcinoma": 1, 
                                           "Infiltrating Lobular Carcinoma": 2, "Mucinous Carcinoma": 3})
df.head()


df["ER status"] = df["ER status"].map({"Positive": 1})
df["PR status"] = df["PR status"].map({"Positive": 1})


df["HER2 status"] = df["HER2 status"].map({"Positive": 1, "Negative": 2})
df["Gender"] = df["Gender"].map({"MALE": 0, "FEMALE": 1})
df["Surgery_type"] = df["Surgery_type"].map({"Other": 1, "Modified Radical Mastectomy": 2, 
                                                 "Lumpectomy": 3, "Simple Mastectomy": 4})
print(df.head())

     Patient_ID   Age  Gender  Protein1  Protein2  Protein3  Protein4  \
0  TCGA-D8-A1XD  36.0       1  0.080353   0.42638   0.54715  0.273680   
1  TCGA-EW-A1OX  43.0       1 -0.420320   0.57807   0.61447 -0.031505   
2  TCGA-A8-A079  69.0       1  0.213980   1.31140  -0.32747 -0.234260   
3  TCGA-D8-A1XR  56.0       1  0.345090  -0.21147  -0.19304  0.124270   
4  TCGA-BH-A0BF  56.0       1  0.221550   1.90680   0.52045 -0.311990   

   Tumour_Stage  Histology  ER status  PR status  HER2 status  Surgery_type  \
0             3          1          1          1            2             2   
1             2          3          1          1            2             3   
2             3          1          1          1            2             1   
3             2          1          1          1            2             2   
4             2          1          1          1            2             1   

  Date_of_Surgery Date_of_Last_Visit Patient_Status  
0       15-Jan-17          19-Jun-17          Alive  
1       26-Apr-17          09-Nov-18           Dead  
2       08-Sep-17          09-Jun-18          Alive  
3       25-Jan-17          12-Jul-17          Alive  
4       06-May-17          27-Jun-19           Dead


status  =  df["Patient_Status"].value_counts()
status

Patient_Status
Alive    255
Dead      62
Name: count, dtype: int64


x = np.array(df[['Age', 'Gender', 'Protein1', 'Protein2', 'Protein3','Protein4', 
                   'Tumour_Stage', 'Histology', 'ER status', 'PR status', 
                   'HER2 status', 'Surgery_type']])
y = np.array(df['Patient_Status'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.10, random_state=42)


model = SVC()
model.fit(x_train, y_train)

SVC()

SVC()


model.score(x_test,y_test)

0.8125


# Prediction
# features = [['Age', 'Gender', 'Protein1', 'Protein2', 'Protein3','Protein4', 'Tumour_Stage', 'Histology', 'ER status', 'PR status', 'HER2 status', 'Surgery_type']]
features = np.array([[74, 1, -0.080353, -0.420320,  0.61447 , -0.273680, 3, 3, 0, 0, 2, 1,]])
print(model.predict(features))

['Alive']


import tkinter as tk
from tkinter import ttk
import numpy as np
from sklearn.svm import SVC

def predict():
    features = [
        float(entries['Age'].get()),
        int(entries['Gender'].get()),
        float(entries['Protein1'].get()),
        float(entries['Protein2'].get()),
        float(entries['Protein3'].get()),
        float(entries['Protein4'].get()),
        int(entries['Tumour Stage'].get()),
        int(entries['Histology'].get()),
        int(entries['ER status'].get()),
        int(entries['PR status'].get()),
        int(entries['HER2 status'].get()),
        int(entries['Surgery Type'].get())
    ]

    features = [features]  # Convert to a 2D array

    print("Input Features:", features)

    prediction = model.predict(features)
    print("Predicted Status:", prediction)

    result_label.config(text=f"Predicted Status: {prediction[0]}")



# Create the main window
window = tk.Tk()
window.title("Breast Cancer Survival Prediction")

# Create labels and entry widgets for each feature
labels = ['Age', 'Gender', 'Protein1', 'Protein2', 'Protein3', 'Protein4',
          'Tumour Stage', 'Histology', 'ER status', 'PR status',
          'HER2 status', 'Surgery Type']

entries = {}

for i, label in enumerate(labels):
    ttk.Label(window, text=label).grid(row=i, column=0, padx=10, pady=5)
    entries[label] = ttk.Entry(window, textvariable=tk.StringVar(), width=20)
    entries[label].grid(row=i, column=1)

# Create a button to trigger prediction
predict_button = ttk.Button(window, text="Predict", command=predict)
predict_button.grid(row=len(labels) + 1, column=0, columnspan=2, pady=10)

# Create a label to display the prediction result
result_label = ttk.Label(window, text="")
result_label.grid(row=len(labels) + 2, column=0, columnspan=2, pady=5)

# Run the main loop
window.mainloop()

Input Features: [[43.0, 1, -0.42032, 0.57807, 0.61447, -0.031505, 2, 3, 1, 1, 2, 3]]
Predicted Status: ['Alive']
Input Features: [[43.0, 1, -0.42032, 0.57807, 0.61447, -0.031505, 2, 3, 1, 1, 2, 3]]
Predicted Status: ['Alive']

	Patient_ID	Age	Gender	Protein1	Protein2	Protein3	Protein4	Tumour_Stage	Histology	ER status	PR status	HER2 status	Surgery_type	Date_of_Surgery	Date_of_Last_Visit	Patient_Status
0	TCGA-D8-A1XD	36.0	FEMALE	0.080353	0.42638	0.54715	0.273680	III	Infiltrating Ductal Carcinoma	Positive	Positive	Negative	Modified Radical Mastectomy	15-Jan-17	19-Jun-17	Alive
1	TCGA-EW-A1OX	43.0	FEMALE	-0.420320	0.57807	0.61447	-0.031505	II	Mucinous Carcinoma	Positive	Positive	Negative	Lumpectomy	26-Apr-17	09-Nov-18	Dead
2	TCGA-A8-A079	69.0	FEMALE	0.213980	1.31140	-0.32747	-0.234260	III	Infiltrating Ductal Carcinoma	Positive	Positive	Negative	Other	08-Sep-17	09-Jun-18	Alive
3	TCGA-D8-A1XR	56.0	FEMALE	0.345090	-0.21147	-0.19304	0.124270	II	Infiltrating Ductal Carcinoma	Positive	Positive	Negative	Modified Radical Mastectomy	25-Jan-17	12-Jul-17	Alive
4	TCGA-BH-A0BF	56.0	FEMALE	0.221550	1.90680	0.52045	-0.311990	II	Infiltrating Ductal Carcinoma	Positive	Positive	Negative	Other	06-May-17	27-Jun-19	Dead